Update app.py

app.py

@@ -6,6 +6,7 @@ import shutil
 import wandb
 import time
 import psutil
+import optuna
 
 from huggingface_hub import login
 
@@ -80,6 +81,12 @@ def format_insights(insights, visuals):
 
 def analyze_data(csv_file, additional_notes=""):
 
+    def objective(trial):
+    learning_rate = trial.suggest_loguniform("learning_rate", 1e-5, 5e-3)
+    batch_size = trial.suggest_categorical("batch_size", [8, 16, 32])
+    num_epochs = trial.suggest_int("num_epochs", 1, 5)
+
+
        # Start timing
     start_time = time.time()
 
@@ -131,6 +138,55 @@ def analyze_data(csv_file, additional_notes=""):
         }
     )
 
+       analysis_result = agent.run(
+        f"""Perform comprehensive analysis with:
+        - Learning Rate: {learning_rate}
+        - Batch Size: {batch_size}
+        - Epochs: {num_epochs}
+        """,
+        additional_args={}
+    )
+
+def tune_hyperparameters(n_trials: int):
+    study = optuna.create_study(direction="minimize")
+    study.optimize(objective, n_trials=n_trials)
+
+    return f"Best Hyperparameters: {study.best_params}"
+
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 📊 AI Data Analysis Agent with Hyperparameter Optimization")
+
+    with gr.Row():
+        with gr.Column():
+            file_input = gr.File(label="Upload CSV Dataset", type="filepath")
+            notes_input = gr.Textbox(label="Dataset Notes (Optional)", lines=3)
+            analyze_btn = gr.Button("Analyze", variant="primary")
+            optuna_trials = gr.Number(label="Number of Hyperparameter Tuning Trials", value=10)
+            tune_btn = gr.Button("Optimize Hyperparameters", variant="secondary")
+
+        with gr.Column():
+            analysis_output = gr.Markdown("### Analysis results will appear here...")
+            optuna_output = gr.Textbox(label="Best Hyperparameters")
+            gallery = gr.Gallery(label="Data Visualizations", columns=2)
+
+    analyze_btn.click(
+        fn=analyze_data,
+        inputs=[file_input, notes_input],
+        outputs=[analysis_output, gallery]
+    )
+
+    tune_btn.click(
+        fn=tune_hyperparameters,
+        inputs=[optuna_trials],
+        outputs=[optuna_output]
+    )
+
+demo.launch(debug=True)
+
+
+   # Assume we minimize some loss value (replace with actual metric)
+    loss = analysis_result.get("loss", 0.1)  # Mock value
+    return loss  # Optuna minimizes the loss
        # Measure execution time and memory usage
     execution_time = time.time() - start_time
     final_memory = process.memory_info().rss / 1024 ** 2  # Convert to MB